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First, we revisit the design principles of ViT and its variants through latency analysis (Sec. ). Following existing work [1]}, we utilize iPhone 12 as the testbed and publicly available CoreML [2]} as the compiler, since the mobile device is widely used and the results can be easily reproduced.

Second, based on our analysis, we identify inefficient designs and operators in ViT and propose a new dimension-consistent design paradigm for vision transformers (Sec. REF ).

Third, starting from a supernet with the new design paradigm, we propose a simple yet effective latency-driven slimming method to obtain a new family of models, namely, EfficientFormers (Sec. REF ). We directly optimize for inference speed instead of MACs or number of parameters [3]}, [4]}, [5]}.

